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Abstract This research paper presents a comprehensive study of the simultaneous localization and mapping (SLAM) algorithm for robot localization and navigation in unknown environments. The SLAM algorithm is a widely used approach for building a map of an environment and estimating the robot’s position within it, which is especially useful in dynamic and unstructured environments. The paper discusses various SLAM techniques, including the Kalman filter (KF) and GraphSLAM algorithms, and their use in probabilistic estimation of the robot’s position and orientation. The paper also explores different path-planning techniques that can be used with the map created by the SLAM algorithm to generate collision-free paths for the robot to navigate toward its goal. The paper also discusses recent advances in deep learning-based SLAM algorithms and their applications in indoor navigation with ORB and RGB-D cameras. The research concludes that SLAM-based robot localization and navigation algorithms are a promising approach for robots navigating in unstructured environments and present various opportunities for future research.
Qiao et al. (Fri,) studied this question.